The prediction of faults that will occur in a system or other piece of equipment is valuable for operational, maintenance, financial and other purposes. For example, the accurate and timely prediction of aircraft engine faults assists with the maintenance of the engine and, in turn, the ability of the aircraft to remain in an operational state. In this regard, the prediction of a fault in advance of its occurrence is useful since the prediction of a fault, particularly well prior to its occurrence, increases the likelihood that maintenance activities can be scheduled at convenient times and can avoid the actual occurrence of the fault which could take the equipment out of service for some period of time and may, in some instances, cause secondary damage to other parts.
A variety of techniques have been developed to predict an impending fault within a system. For example, diagnostic models have been constructed utilizing parametric sensor data to predict an impending fault. With respect to an aircraft, the parametric data may include both raw sensor measurements from the engine or airframe as well as sensor readings that have been corrected to account for flight conditions, such as altitude, ambient temperature, etc. In conjunction with an aircraft engine, for example, the parametric data may include the exhaust gas temperature, fuel flow, engine oil pressure and engine core speed. While parametric data may be useful to predict an impending fault, parametric data can be voluminous and relatively inefficient to compress such that commercial aircraft generally preserve only a few snapshots of parametric data at different intervals during a flight, e.g., takeoff, cruise, and descent.
Other fault prediction techniques have relied upon non-parametric data, such as the data generated in response to built-in tests that produce error log messages. For example, non-parametric error logs can be maintained which indicate when parametric measurements are beyond predefined thresholds, when certain demanded actuator positions are not reached or, more generally, when a certain subsystem behaves outside of predefined operating parameters. The resulting non-parametric error logs are a collection of binary flags which can efficiently be compressed, and so are often recorded for the duration of a flight for later analysis. Thus, the nonparametric data provide insight into the system status over an entire operational cycle, such as over an entire flight, as opposed to only at certain intervals.
With respect to aircraft, the parametric data and non-parametric data have typically been evaluated independent of one another. While such independent evaluation provides some useful information in regard to the prediction of faults within a system, the evaluation of each type of data may sometimes be limited. As such, a technique for predicting faults within an aircraft engine has been proposed in which both parametric data and non-parametric data are combined. In this regard, the non-parametric data may transformed into parametric data in a variety of manners including message decaying and cumulative index techniques as described by Neil Eklund, et al., “A Data Fusion Approach for Aircraft Engine Fault Diagnostics,” Proceedings of ASME Turbo Expo 2007, GT2007-27941 (May 2007). These transformed non-parametric data may then be integrated with the parametric data for analysis by traditional methods. As such, the resulting diagnostic model can have the benefit of both the parametric and non-parametric data which may be beneficial to the prediction of impending faults in a reliable manner with fewer false alarms than if either the parametric or nonparametric data were considered alone.
While the combination of the parametric data and the non-parametric data may provide improvements in regard to the prediction of an impending fault, it would still be desirable to further improve upon the techniques for predicting faults in an accurate and reliable manner. In this regard, it is certainly desirable to reduce the instances in which a fault occurs without any advance warning. With respect to aircraft, for example, it would be desirable to not only improve upon the techniques for predicting faults so as to reduce the instances in which equipment fails without warning, but also to improve upon the timing with which those faults are detected since significant operational, maintenance and financial issues may be created if the faults are detected only slightly before the occurrence of the fault, or not detected until the fault has manifested, as opposed to the detection of a fault well in advance of its occurrence. In this regard, the prediction of an imminent fault may cause maintenance actions to be taken immediately, thereby potentially causing service disruptions, such as delayed or cancelled flights, and possibly increasing the cost of the maintenance activity since the labor, spares and shop time will need to be quickly allocated. Conversely, if a fault can be detected well prior to its occurrence, the necessary maintenance actions can be scheduled, thereby potentially reducing the cost of the maintenance, permitting the maintenance to be performed when the aircraft is not otherwise scheduled to be in service, and ensuring the resources required for the service—such as parts, mechanics, and service bays—are available. Accordingly, it would be desirable to not only improve upon the techniques for predicting faults in an accurate and reliable manner, but also to permit faults to be predicted further in advance of the occurrence of the faults such that appropriate maintenance activities can be scheduled in an efficient and economic manner.